@Reference(authors="H. Akaike", title="On entropy maximization principle", booktitle="Application of statistics, 1977, North-Holland") public class AkaikeInformationCriterion extends AbstractKMeansQualityMeasure<NumberVector>
H. Akaike
On entropy maximization principle
Application of statistics, 1977, North-Holland
D. Pelleg, A. Moore:
X-means: Extending K-means with Efficient Estimation on the Number of
Clusters
In: Proceedings of the 17th International Conference on Machine Learning
(ICML 2000)
Constructor and Description |
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AkaikeInformationCriterion() |
Modifier and Type | Method and Description |
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boolean |
ascending()
Use ascending or descending ordering.
|
boolean |
isBetter(double currentCost,
double bestCost)
Compare two scores.
|
<V extends NumberVector> |
quality(Clustering<? extends MeanModel> clustering,
NumberVectorDistanceFunction<? super V> distanceFunction,
Relation<V> relation)
Calculates and returns the quality measure.
|
logLikelihood, logLikelihoodAlternate, numberOfFreeParameters, numPoints, varianceOfCluster
public <V extends NumberVector> double quality(Clustering<? extends MeanModel> clustering, NumberVectorDistanceFunction<? super V> distanceFunction, Relation<V> relation)
KMeansQualityMeasure
V
- Actual vector type (could be a subtype of O!)clustering
- Clustering to analyzedistanceFunction
- Distance function to use (usually Euclidean or
squared Euclidean!)relation
- Relation for accessing objectspublic boolean ascending()
KMeansQualityMeasure
true
when larger scores are better.public boolean isBetter(double currentCost, double bestCost)
KMeansQualityMeasure
currentCost
- New (candiate) cost/scorebestCost
- Existing best cost/score (may be NaN
)true
when the new score is better, or the old score is
NaN
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